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This paper presents a revised method for keyword search from handwritten digital ink in comparison with the previous system. We adopt a search method using noise reduction. Experiments on digital ink databases show that the revised method typically improves the systempsilas overall accuracy (f-measure) from 0.653 to
In this paper, we proposed a new 3D object retrieval method based on the visual keywords. In our method, the visual keywords are generated from the clusters of relative angle context distribution, which provides a statistical shape context that captures local shape characters and is also rotational and scale invariant
paper, we propose a Bayesian approach to region-based image annotation, which integrates the content-based search and context into a unified framework. The content-based search selects representative keywords by matching an unlabeled image with the labeled ones followed by a weighted keyword ranking, which are in turn used
As the search technology rapidly developed, nowadays, main search engines are already able to meet users basic search desire. However, current search algorithms or methodologies mostly depend on keywords matching process, which could be effective for text search while not efficient for keywords-lacking or non-text
where our approach is tested on images retrieved from Google keyword based image search engine. The results show that a combination of our approach as a local image descriptor with another global descriptor outperforms other approaches.
Content-based image retrieval (CBIR) has been adopted as a complementary technique to the keyword-based image search. Relevance feedback (RFB) is considered as an effective means to bridge the gap between the designated features and the run-time semantics on a CBIR system. Like many other interactive system, a good
Recently, the development of 3D model database systems and retrieval components are becoming increasingly important due to a rapidly growing amount of available 3D models. This has made the retrieval for specific 3D models become a vital issue. Unfortunately, traditional keyword searching techniques are not always
which regions of interest (ROIs) become highlighted. These UIMs are used to establish and reinforce keyword-to-image relations. A tentative application for semantic annotation is also presented.
This paper presents a case study of an image retrieval system based on a notion of similarity between images in a multimedia database and where a user request can be an image file or a keyword. The CBIR (content based image retrieval) system, the current system of search for information (SSI) -e.g. PEIR, MIRC, MIR
There are currently two interface types for searching and browsing large image collections: keyword-based image retrieval (KBIR), and content-based image retrieval (CBIR). The KBIR system searches images according to the text of keyword annotated on images. This method is simple and relative effective to the query
The amount of multimedia information is rapidly increasing due to digital cameras. To interpret semantic of image, many researcher use keywords as textual annotation. Image semantic information retrieval became attractive for many peoples. Concept recognition is a key problem in semantic information searching. A new
With the rapid development of technology of multimedia, the traditional information retrieval techniques based on keywords are not sufficient, content - based image retrieval (CBIR) has been an active research topic. A new content based image retrieval method using the feature analysis of edge extraction and median
Traditional image classification techniques are based on the analysis of low-level visual features or on textual information. In this paper, we describe a novel solution which tries to improve image analysis and processing algorithms by incorporating keywords and textual annotation produced by humans in a folksonomy
A kernel PCA-based semantic feature estimation approach for similar image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image. First, our method performs semantic clustering of the database images and
The task of ad hoc photographic image retrieval in ImageCLEF 2007 international benchmark is to retrieve relevant images in the database to the user query formulated as keywords and image examples. This paper presents rich representation and indexing technologies exploited in our system that participated in ImageCLEF
sets of keywords is used and a dynamic approach is presented for updating the semantic network and semantic contents of the images in an interactive way. The proposed approach, certainly, executes a novel kind of long term learning-based on user's opinion in the several interactions with system, and relates the behavior
Most of the algorithms proposed in the literature deal with the problem of digital image retrieval. To interpret semantic of image, many researcher use keywords as textual annotation. Concept recognition is a key problem in semantic information searching. In order to be effective and efficient, we proposed a parallel
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